def train(args):
# Make environments, CFR only supports Leduc Holdem
env = rlcard.make('leduc-holdem', config={'seed': 0, 'allow_step_back':True})
eval_env = rlcard.make('leduc-holdem', config={'seed': 0})
# Seed numpy, torch, random
set_seed(args.seed)
# Initilize CFR Agent
agent = CFRAgent(env, os.path.join(args.log_dir, 'cfr_model'))
agent.load() # If we have saved model, we first load the model
# Evaluate CFR against random
eval_env.set_agents([agent, RandomAgent(num_actions=env.num_actions)])
# Start training
with Logger(args.log_dir) as logger:
for episode in range(args.num_episodes):
agent.train()
print('\rIteration {}'.format(episode), end='')
# Evaluate the performance. Play with Random agents.
if episode % args.evaluate_every == 0:
agent.save() # Save model
logger.log_performance(env.timestep, tournament(eval_env, args.num_eval_games)[0])
# Get the paths
csv_path, fig_path = logger.csv_path, logger.fig_path
# Plot the learning curve
plot_curve(csv_path, fig_path, 'cfr')
def train_leduc():
# Make environment and enable human mode
env = rlcard.make('leduc-holdem', config={'seed': 0, 'allow_step_back':True})
eval_env = rlcard.make('leduc-holdem', config={'seed': 0})
# Set the iterations numbers and how frequently we evaluate the performance and save model
evaluate_every = 100
save_plot_every = 1000
evaluate_num = 10000
episode_num = 10000
# The paths for saving the logs and learning curves
log_dir = './experiments/leduc_holdem_cfr_result/'
# Set a global seed
set_global_seed(0)
model_path = 'models/leduc_holdem_cfr'
# Initilize CFR Agent
agent = CFRAgent(env,model_path=model_path)
agent.load() # If we have saved model, we first load the model
# Evaluate CFR against pre-trained NFSP
eval_env.set_agents([agent, models.load('leduc-holdem-nfsp').agents[0]])
# Init a Logger to plot the learning curve
logger = Logger(log_dir)
for episode in range(episode_num):
agent.train()
print('\rIteration {}'.format(episode), end='')
# Evaluate the performance. Play with NFSP agents.
if episode % evaluate_every == 0:
agent.save() # Save model
logger.log_performance(env.timestep, tournament(eval_env, evaluate_num)[0])
# Close files in the logger
logger.close_files()
# Plot the learning curve
logger.plot('CFR')
# The paths for saving the logs and learning curves
log_dir = './experiments/leduc_holdem_cfr_result/'
# Set a global seed
set_global_seed(0)
# Initilize CFR Agent
agent = CFRAgent(env)
agent.load() # If we have saved model, we first load the model
# Evaluate CFR against pre-trained NFSP
eval_env.set_agents([agent, models.load('leduc-holdem-nfsp').agents[0]])
# Init a Logger to plot the learning curve
logger = Logger(log_dir)
for episode in range(episode_num):
agent.train()
print('\rIteration {}'.format(episode), end='')
# Evaluate the performance. Play with NFSP agents.
if episode % evaluate_every == 0:
agent.save() # Save model
logger.log_performance(env.timestep,
tournament(eval_env, evaluate_num)[0])
# Close files in the logger
logger.close_files()
# Plot the learning curve
logger.plot('CFR')